from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HpMatchReporting(other_library="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 14 predictions (58.33%).
The observed differences can be found in the diff_accuracy_scores column.
The chosen difference threshold is 0.001 for accuracy_score.
See details in the dataframe below.

	estimator	function	diff_accuracy_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup
1	KNeighborsClassifier_brute_force	predict	0.024	100000	1000	100	1.994884	0.172649	NaN	0.000401	0.001995	brute	-1	1	0.663	0.200178	0.006385	0.687	9.965554	9.970623
4	KNeighborsClassifier_brute_force	predict	0.015	100000	1000	100	2.951273	0.047169	NaN	0.000271	0.002951	brute	-1	5	0.757	0.208435	0.008071	0.742	14.159178	14.169788
7	KNeighborsClassifier_brute_force	predict	0.007	100000	1000	100	2.199261	0.025263	NaN	0.000364	0.002199	brute	1	100	0.882	0.254386	0.009528	0.875	8.645381	8.651443
8	KNeighborsClassifier_brute_force	predict	1.000	100000	1	100	0.020690	0.001177	NaN	0.000039	0.020690	brute	1	100	1.000	0.010163	0.001924	0.000	2.035822	2.071966
10	KNeighborsClassifier_brute_force	predict	0.007	100000	1000	100	2.924734	0.059220	NaN	0.000274	0.002925	brute	-1	100	0.882	0.252963	0.010147	0.875	11.561900	11.571198
11	KNeighborsClassifier_brute_force	predict	1.000	100000	1	100	0.027676	0.005492	NaN	0.000029	0.027676	brute	-1	100	1.000	0.009000	0.001279	0.000	3.074985	3.105884
13	KNeighborsClassifier_brute_force	predict	0.015	100000	1000	100	2.188334	0.040193	NaN	0.000366	0.002188	brute	1	5	0.757	0.206100	0.009081	0.742	10.617819	10.628121
16	KNeighborsClassifier_brute_force	predict	0.024	100000	1000	100	1.223682	0.020244	NaN	0.000654	0.001224	brute	1	1	0.663	0.204675	0.011459	0.687	5.978645	5.988009
19	KNeighborsClassifier_brute_force	predict	0.071	100000	1000	2	1.747191	0.032236	NaN	0.000009	0.001747	brute	-1	1	0.896	0.031046	0.001858	0.967	56.277871	56.378567
22	KNeighborsClassifier_brute_force	predict	0.052	100000	1000	2	2.764815	0.041363	NaN	0.000006	0.002765	brute	-1	5	0.922	0.033079	0.001158	0.974	83.581296	83.632454
25	KNeighborsClassifier_brute_force	predict	0.046	100000	1000	2	2.132174	0.070030	NaN	0.000008	0.002132	brute	1	100	0.929	0.075151	0.002130	0.975	28.371885	28.383277
28	KNeighborsClassifier_brute_force	predict	0.046	100000	1000	2	2.782344	0.052769	NaN	0.000006	0.002782	brute	-1	100	0.929	0.075194	0.003862	0.975	37.002119	37.050903
31	KNeighborsClassifier_brute_force	predict	0.052	100000	1000	2	2.130075	0.029014	NaN	0.000008	0.002130	brute	1	5	0.922	0.031093	0.002596	0.974	68.506535	68.744841
34	KNeighborsClassifier_brute_force	predict	0.071	100000	1000	2	1.072366	0.010114	NaN	0.000015	0.001072	brute	1	1	0.896	0.030933	0.002092	0.967	34.667306	34.746517

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.001	6.578	0.0	-1	1	0.051	0.005	0.240	0.241	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.001	6.768	0.0	-1	5	0.050	0.001	0.236	0.236	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.001	6.941	0.0	1	100	0.049	0.002	0.237	0.237	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.001	6.794	0.0	-1	100	0.055	0.006	0.214	0.215	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	6.791	0.0	1	5	0.051	0.002	0.232	0.232	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	6.778	0.0	1	1	0.050	0.001	0.236	0.236	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.326	0.0	-1	1	0.010	0.001	0.506	0.507	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.315	0.0	-1	5	0.011	0.002	0.470	0.480	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.305	0.0	1	100	0.010	0.001	0.527	0.528	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.334	0.0	-1	100	0.009	0.000	0.509	0.510	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.322	0.0	1	5	0.009	0.002	0.524	0.532	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.324	0.0	1	1	0.009	0.001	0.567	0.568	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.995	0.173	0.000	0.002	-1	1	0.200	0.006	9.966	9.971	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.002	0.000	0.023	-1	1	0.008	0.000	2.728	2.730	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.951	0.047	0.000	0.003	-1	5	0.208	0.008	14.159	14.170	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.025	0.003	0.000	0.025	-1	5	0.009	0.001	2.966	2.977	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.199	0.025	0.000	0.002	1	100	0.254	0.010	8.645	8.651	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.021	0.001	0.000	0.021	1	100	0.010	0.002	2.036	2.072	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.925	0.059	0.000	0.003	-1	100	0.253	0.010	11.562	11.571	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.028	0.005	0.000	0.028	-1	100	0.009	0.001	3.075	3.106	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.188	0.040	0.000	0.002	1	5	0.206	0.009	10.618	10.628	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.021	0.001	0.000	0.021	1	5	0.008	0.000	2.541	2.543	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.224	0.020	0.001	0.001	1	1	0.205	0.011	5.979	5.988	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.021	0.002	0.000	0.021	1	1	0.008	0.001	2.480	2.502	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.747	0.032	0.000	0.002	-1	1	0.031	0.002	56.278	56.379	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.005	0.003	0.000	0.005	-1	1	0.001	0.000	6.086	6.290	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.765	0.041	0.000	0.003	-1	5	0.033	0.001	83.581	83.632	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.007	0.005	0.000	0.007	-1	5	0.001	0.000	9.223	9.357	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.132	0.070	0.000	0.002	1	100	0.075	0.002	28.372	28.383	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	100	0.001	0.000	3.849	3.872	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.782	0.053	0.000	0.003	-1	100	0.075	0.004	37.002	37.051	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.007	0.004	0.000	0.007	-1	100	0.001	0.000	9.114	9.171	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.130	0.029	0.000	0.002	1	5	0.031	0.003	68.507	68.745	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	5	0.001	0.000	4.435	4.491	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.072	0.010	0.000	0.001	1	1	0.031	0.002	34.667	34.747	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	0.000	0.002	1	1	0.001	0.000	2.723	2.742	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 12 predictions (50.0%).
The observed differences can be found in the diff_accuracy_scores column.
The chosen difference threshold is 0.001 for accuracy_score.
See details in the dataframe below.

	estimator	function	diff_accuracy_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup
1	KNeighborsClassifier_kd_tree	predict	0.019	1000000	1000	10	0.891720	1.171892	NaN	0.000090	0.000892	kd_tree	-1	1	0.929	0.113312	0.004687	0.910	7.869611	7.876342
4	KNeighborsClassifier_kd_tree	predict	0.005	1000000	1000	10	1.141847	0.397180	NaN	0.000070	0.001142	kd_tree	-1	5	0.946	0.196773	0.002756	0.941	5.802866	5.803435
7	KNeighborsClassifier_kd_tree	predict	0.011	1000000	1000	10	5.498338	0.533157	NaN	0.000015	0.005498	kd_tree	1	100	0.951	0.594319	0.007156	0.940	9.251497	9.252167
10	KNeighborsClassifier_kd_tree	predict	0.011	1000000	1000	10	3.021899	0.291408	NaN	0.000026	0.003022	kd_tree	-1	100	0.951	0.568129	0.010698	0.940	5.319035	5.319978
13	KNeighborsClassifier_kd_tree	predict	0.005	1000000	1000	10	1.672402	0.245720	NaN	0.000048	0.001672	kd_tree	1	5	0.946	0.194308	0.005913	0.941	8.606966	8.610950
16	KNeighborsClassifier_kd_tree	predict	0.019	1000000	1000	10	0.904323	0.193756	NaN	0.000088	0.000904	kd_tree	1	1	0.929	0.105632	0.002068	0.910	8.561059	8.562700
19	KNeighborsClassifier_kd_tree	predict	0.012	1000	1000	2	0.033206	0.020860	NaN	0.000482	0.000033	kd_tree	-1	1	0.891	0.000454	0.000056	0.879	73.202972	73.756421
22	KNeighborsClassifier_kd_tree	predict	0.006	1000	1000	2	0.024285	0.001525	NaN	0.000659	0.000024	kd_tree	-1	5	0.911	0.000712	0.000066	0.905	34.119666	34.268086
25	KNeighborsClassifier_kd_tree	predict	0.023	1000	1000	2	0.039488	0.008824	NaN	0.000405	0.000039	kd_tree	1	100	0.894	0.005072	0.000353	0.917	7.785052	7.803839
28	KNeighborsClassifier_kd_tree	predict	0.023	1000	1000	2	0.041841	0.007527	NaN	0.000382	0.000042	kd_tree	-1	100	0.894	0.005072	0.000341	0.917	8.249540	8.268117
31	KNeighborsClassifier_kd_tree	predict	0.006	1000	1000	2	0.021844	0.002233	NaN	0.000732	0.000022	kd_tree	1	5	0.911	0.000820	0.000230	0.905	26.651468	27.684826
34	KNeighborsClassifier_kd_tree	predict	0.012	1000	1000	2	0.021050	0.001284	NaN	0.000760	0.000021	kd_tree	1	1	0.891	0.000511	0.000274	0.879	41.208238	46.778862

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.358	0.129	0.024	0.0	-1	1	0.833	0.130	4.032	4.081	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.924	0.171	0.020	0.0	-1	5	0.821	0.143	4.782	4.854	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.863	0.168	0.021	0.0	1	100	0.776	0.027	4.978	4.981	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.790	0.154	0.021	0.0	-1	100	0.761	0.015	4.982	4.983	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.797	0.108	0.021	0.0	1	5	0.734	0.008	5.172	5.173	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.804	0.053	0.021	0.0	1	1	0.746	0.026	5.097	5.100	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	0.014	0.0	-1	1	0.004	0.002	0.279	0.329	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.024	0.0	-1	5	0.002	0.002	0.390	0.595	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.026	0.0	1	100	0.001	0.001	0.530	0.581	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.027	0.0	-1	100	0.001	0.001	0.532	0.584	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.029	0.0	1	5	0.001	0.000	0.558	0.562	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.024	0.0	1	1	0.001	0.000	0.771	0.775	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.892	1.172	0.000	0.001	-1	1	0.113	0.005	7.870	7.876	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.000	0.000	0.003	-1	1	0.000	0.000	10.288	10.685	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.142	0.397	0.000	0.001	-1	5	0.197	0.003	5.803	5.803	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	5	0.000	0.000	8.218	8.514	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	5.498	0.533	0.000	0.005	1	100	0.594	0.007	9.251	9.252	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	1	100	0.001	0.000	3.740	3.891	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.022	0.291	0.000	0.003	-1	100	0.568	0.011	5.319	5.320	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.006	0.002	0.000	0.006	-1	100	0.001	0.000	7.424	7.784	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.672	0.246	0.000	0.002	1	5	0.194	0.006	8.607	8.611	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.000	0.000	0.002	1	5	0.000	0.000	3.882	4.154	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.904	0.194	0.000	0.001	1	1	0.106	0.002	8.561	8.563	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	1	0.000	0.000	3.603	3.871	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.033	0.021	0.000	0.000	-1	1	0.000	0.000	73.203	73.756	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.003	0.000	0.000	0.003	-1	1	0.000	0.000	31.704	32.592	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.024	0.002	0.001	0.000	-1	5	0.001	0.000	34.120	34.268	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	5	0.000	0.000	22.681	23.367	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.039	0.009	0.000	0.000	1	100	0.005	0.000	7.785	7.804	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	100	0.000	0.000	5.350	5.531	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.042	0.008	0.000	0.000	-1	100	0.005	0.000	8.250	8.268	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.001	0.000	0.002	-1	100	0.000	0.000	20.554	21.261	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.022	0.002	0.001	0.000	1	5	0.001	0.000	26.651	27.685	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	5	0.000	0.000	7.978	8.205	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.021	0.001	0.001	0.000	1	1	0.001	0.000	41.208	46.779	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	1	0.000	0.000	6.808	7.016	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.568	0.089	30	0.028	0.0	random	0.470	0.043	1.208	1.213	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.617	0.023	30	0.026	0.0	k-means++	0.500	0.028	1.236	1.238	See	See
6	KMeans_tall	fit	1000000	1000000	100	5.927	0.253	30	0.135	0.0	random	2.795	0.031	2.120	2.120	See	See
9	KMeans_tall	fit	1000000	1000000	100	6.242	0.125	30	0.128	0.0	k-means++	2.935	0.027	2.126	2.127	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.010	0.000	random	0.0	0.0	8.407	13.110	See	See
2	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	random	0.0	0.0	8.556	12.540	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.000	30	0.010	0.000	k-means++	0.0	0.0	10.844	12.057	See	See
5	KMeans_tall	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	k-means++	0.0	0.0	15.926	16.614	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.001	30	0.425	0.000	random	0.0	0.0	6.255	6.696	See	See
8	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.001	0.002	random	0.0	0.0	13.624	14.047	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.424	0.000	k-means++	0.0	0.0	6.106	6.324	See	See
11	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.001	0.002	k-means++	0.0	0.0	13.173	13.837	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 4 predictions (50.0%).
The observed differences can be found in the diff_adjusted_rand_scores column.
The chosen difference threshold is 0.001 for adjusted_rand_score.
See details in the dataframe below.

	estimator	function	diff_adjusted_rand_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	init	max_iter	n_clusters	n_init	tol	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup
1	KMeans_short	predict	0.001090	10000	1000	2	0.002212	0.000313	20	0.007234	0.000002	full	random	20	300	1	1.000000e-16	0.000126	0.000646	0.000094	-0.000965	3.423656	3.459549
4	KMeans_short	predict	0.001995	10000	1000	2	0.002235	0.000322	20	0.007160	0.000002	full	k-means++	20	300	1	1.000000e-16	0.001245	0.000506	0.000070	-0.000750	4.418226	4.459899
7	KMeans_short	predict	0.015034	10000	1000	100	0.002749	0.000200	20	0.290985	0.000003	full	random	20	300	1	1.000000e-16	0.278733	0.001099	0.000149	0.293767	2.501037	2.523774
10	KMeans_short	predict	0.060044	10000	1000	100	0.003040	0.000426	20	0.263146	0.000003	full	k-means++	20	300	1	1.000000e-16	0.317011	0.001151	0.000097	0.256968	2.642407	2.651786

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.088	0.002	20	0.002	0.0	random	0.033	0.002	2.694	2.701	See	See
3	KMeans_short	fit	10000	10000	2	0.254	0.004	20	0.001	0.0	k-means++	0.100	0.005	2.544	2.547	See	See
6	KMeans_short	fit	10000	10000	100	0.228	0.005	20	0.035	0.0	random	0.127	0.002	1.797	1.797	See	See
9	KMeans_short	fit	10000	10000	100	0.691	0.015	20	0.012	0.0	k-means++	0.352	0.008	1.962	1.963	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.000	20	0.007	0.000	random	0.001	0.0	3.424	3.460	See	See
2	KMeans_short	predict	10000	1	2	0.002	0.000	20	0.000	0.002	random	0.000	0.0	14.208	14.543	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.000	20	0.007	0.000	k-means++	0.001	0.0	4.418	4.460	See	See
5	KMeans_short	predict	10000	1	2	0.002	0.000	20	0.000	0.002	k-means++	0.000	0.0	12.309	13.268	See	See
7	KMeans_short	predict	10000	1000	100	0.003	0.000	20	0.291	0.000	random	0.001	0.0	2.501	2.524	See	See
8	KMeans_short	predict	10000	1	100	0.002	0.000	20	0.000	0.002	random	0.000	0.0	11.056	11.241	See	See
10	KMeans_short	predict	10000	1000	100	0.003	0.000	20	0.263	0.000	k-means++	0.001	0.0	2.642	2.652	See	See
11	KMeans_short	predict	10000	1	100	0.002	0.001	20	0.000	0.002	k-means++	0.000	0.0	11.047	11.235	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 2 predictions (50.0%).
The observed differences can be found in the diff_accuracy_scores column.
The chosen difference threshold is 0.001 for accuracy_score.
See details in the dataframe below.

	estimator	function	diff_accuracy_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup
1	LogisticRegression	predict	0.01	1000000	1000	100	0.000412	0.000413	[20]	1.940387	4.122889e-07	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.56	0.000837	0.001468	0.55	0.492486	0.994060
4	LogisticRegression	predict	0.07	1000	100	10000	0.001854	0.000168	[26]	4.316066	1.853540e-05	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.35	0.004156	0.001500	0.28	0.446004	0.474164

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	11.608	0.596	[20]	0.069	0.000	1.922	0.019	6.038	6.039	See	See
3	LogisticRegression	fit	1000	1000	10000	1.050	0.757	[26]	0.076	0.001	0.769	0.035	1.366	1.367	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.000	0.0	[20]	1.940	0.0	0.001	0.001	0.492	0.994	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.0	[20]	0.010	0.0	0.000	0.000	0.528	0.535	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.0	[26]	4.316	0.0	0.004	0.001	0.446	0.474	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.0	[26]	0.727	0.0	0.001	0.000	0.163	0.164	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 1 prediction (25.0%).
The observed differences can be found in the diff_r2_scores column.
The chosen difference threshold is 0.001 for r2_score.
See details in the dataframe below.

	estimator	function	diff_r2_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	r2_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	r2_score_sklearnex	speedup	std_speedup
1	Ridge	predict	0.039624	1000	1000	10000	0.010012	0.000279	NaN	7.990133	0.00001	1.0	True	True	NaN	deprecated	NaN	auto	0.001	0.082567	0.017062	0.001144	0.122191	0.586832	0.588149

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.187	0.004	0.429	0.0	0.196	0.004	0.950	0.950	See	See
3	Ridge	fit	1000000	1000000	100	1.270	0.229	0.630	0.0	0.327	0.264	3.883	4.989	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.01	0.0	7.990	0.0	0.017	0.001	0.587	0.588	See	See
2	Ridge	predict	1000	1	10000	0.00	0.0	1.216	0.0	0.000	0.000	0.683	0.730	See	See
4	Ridge	predict	1000000	1000	100	0.00	0.0	5.281	0.0	0.000	0.000	0.424	0.686	See	See
5	Ridge	predict	1000000	1	100	0.00	0.0	0.015	0.0	0.000	0.000	0.531	0.562	See	See